{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# python时间和日期\n",
    "1、时间戳方式，相对于1970.1.1 00:00:00以秒计算的偏移量\n",
    "\n",
    "2、时间字符串方式，例如：2016-04-10 21:26:25\n",
    "\n",
    "3、结构化的时间方式，例如python中的datetime、struct_time等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x37Bhg5hz6dIlaFioV/D444/DwVIgKO7HS+Wj2KtXr4rpZcuWiTT94sWLK1as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\nAADA9wAAAIDvAQAA8D0AAAC4kP8PWC8l6EDxjJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=u\"..\\images\\python时间和日期.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 格式化符号\n",
    "\n",
    "  %y 两位数的年份表示（00-99）\n",
    "  \n",
    "  %Y 四位数的年份表示（000-9999）\n",
    "  \n",
    "  %m 月份（01-12）\n",
    "  \n",
    "  %d 月内中的一天（0-31）\n",
    "  \n",
    "  %H 24小时制小时数（0-23）\n",
    "  \n",
    "  %I 12小时制小时数（01-12） \n",
    "  \n",
    "  %M 分钟数（00=59）\n",
    "  \n",
    "  %S 秒（00-59）\n",
    "  \n",
    "  %a 本地简化星期名称\n",
    "  \n",
    "  %A 本地完整星期名称\n",
    "  \n",
    "  %b 本地简化的月份名称\n",
    "  \n",
    "  %B 本地完整的月份名称\n",
    "  \n",
    "  %c 本地相应的日期表示和时间表示\n",
    "  \n",
    "  %j 年内的一天（001-366）\n",
    "  \n",
    "  %p 本地A.M.或P.M.的等价符\n",
    "  \n",
    "  %U 一年中的星期数（00-53）星期天为星期的开始\n",
    "  \n",
    "  %w 星期（0-6），星期天为星期的开始\n",
    "  \n",
    "  %W 一年中的星期数（00-53）星期一为星期的开始\n",
    "  \n",
    "  %x 本地相应的日期表示\n",
    "  \n",
    "  %X 本地相应的时间表示\n",
    "  \n",
    "  %Z 当前时区的名称\n",
    "  \n",
    "  %% %号本身 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'datetime.datetime'>\n",
      "2016-04-10 23:02:35.178000\n",
      "2016-04-10\n",
      "23:02:35.178000\n",
      "364 days, 1:00:00\n",
      "time.struct_time(tm_year=2016, tm_mon=4, tm_mday=10, tm_hour=23, tm_min=2, tm_sec=35, tm_wday=6, tm_yday=101, tm_isdst=-1)\n",
      "23:02:35 04/10/2016\n"
     ]
    }
   ],
   "source": [
    "from datetime import *\n",
    "\n",
    "#创建datetime对象\n",
    "now = datetime.now()                        #产生当前日期和时间\n",
    "print type(now)\n",
    "print now\n",
    "print now.date()\n",
    "print now.time()\n",
    "\n",
    "#year, month, day, hour=None, minute=None, second=None, microsecond=None, tzinfo=None\n",
    "year_after =  datetime(year=now.year + 1, month=now.month, \n",
    "                       day=now.day, minute=now.minute, second=now.second, microsecond=now.microsecond, tzinfo=now.tzinfo) #产生自定义日期\n",
    "\n",
    "print year_after - now                      #datetime +/- datetime => timedelta\n",
    "print now.timetuple()                       #datetime => time.struct_time\n",
    "print now.strftime('%H:%M:%S %m/%d/%Y')     #datetime => string time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-04-10 23:02:35.343000\n"
     ]
    }
   ],
   "source": [
    "from datetime import *                  \n",
    "#days=0, seconds=0, microseconds=0, milliseconds=0,minutes=0, hours=0, weeks=0\n",
    "td = timedelta(days=365)                 #产生365天时间间隔\n",
    "year_after = datetime.now() + td         #datetome + timedelta => datetime\n",
    "print year_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## strig time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Nov 11 11:11:11 2008\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2016, 4, 10, 20, 22, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import *\n",
    "import time\n",
    "print time.asctime((2008,11,11,11,11,11,0,0,0))\n",
    "datetime.strptime(\"2016-04-10 20:22:10\", \"%Y-%m-%d %H:%M:%S\") #string time => datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## time.struct_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=1970, tm_mon=1, tm_mday=1, tm_hour=8, tm_min=0, tm_sec=1, tm_wday=3, tm_yday=1, tm_isdst=0)\n",
      "time.struct_time(tm_year=2016, tm_mon=4, tm_mday=10, tm_hour=23, tm_min=2, tm_sec=35, tm_wday=6, tm_yday=101, tm_isdst=0)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "tm = time.struct_time((2008,11,11,11,11,11,0,0,0))\n",
    "\n",
    "print time.localtime(1)\n",
    "print time.localtime()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2016, tm_mon=4, tm_mday=10, tm_hour=15, tm_min=2, tm_sec=35, tm_wday=6, tm_yday=101, tm_isdst=0)\n",
      "time.struct_time(tm_year=2016, tm_mon=4, tm_mday=10, tm_hour=23, tm_min=2, tm_sec=35, tm_wday=6, tm_yday=101, tm_isdst=0)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "time.time() #当前时间戳\n",
    "\n",
    "print time.gmtime()\n",
    "print time.localtime()"
   ]
  }
 ],
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